Seasonality in macroeconomic time series can obscure movements of other components in a series that are operationally more important for economic and econometric analyses. Indeed, in practice one often prefers to work with seasonally adjusted data to assess the current state of the economy and its future course. Recently, two most widely used seasonal adjustment methods, Census X-12-ARIMA and TRAMO-SEATS, merged into X-13ARIMA-SEATS to become a new industry standard. In this paper, we compare and contrast X-13ARIMA-SEATS with a seasonal adjustment program called CAMPLET, an acronym of its tuning parameters. CAMPLET consists of a simple adaptive procedure which separates the seasonal component and the non-seasonal component from an observed time series. Once this process has been carried out there will be no need to revise these components at a later stage when more observations become available, in contrast with other seasonal adjustment methods. The paper briefly reviews of X-13ARIMA-SEATS and describes the main features of CAMPLET. We evaluate the outcomes of both methods in a controlled simulation framework using a variety of processes. Finally, we apply the X-13ARIMA-SEATS and CAMPLET methods to three time series: U.S. non-farm payroll employment, operational income of Ahold, and real GDP in the Netherlands.